Python for ML and Data
3-4 weeksSkills You'll Build
Python programming fundamentalsNumPy and Pandas for dataPython testing frameworks (pytest)Data validation and quality checksWorking with ML libraries
Test Engineer → MLOps Engineer
Test Engineer to MLOps Engineer: From Testing to ML Operations
Your testing expertise is invaluable in MLOps. This path transforms your understanding of quality assurance, test automation, and CI/CD into ML operations skills. Build the pipelines, monitoring, and validation systems that ensure ML models work reliably in production.
5-8 months
Prerequisites
- Experience in software testing or QA engineering
- Proficiency in test automation (Python, Selenium, etc.)
- Understanding of CI/CD pipelines
- Experience with version control and Git workflows
- Familiarity with containerization (Docker)
Your Learning Path
ML Fundamentals for Operations
3-4 weeksSkills You'll Build
Machine learning conceptsModel training and evaluationUnderstanding model artifactsData pipelines for MLExperiment tracking basics
ML Testing and Validation
4-5 weeksSkills You'll Build
Testing ML models (unit, integration, system)Data validation and quality testingModel performance testingA/B testing for MLTest automation for ML pipelines
MLOps Pipelines and CI/CD
4-5 weeksSkills You'll Build
ML pipeline orchestrationCI/CD for ML (MLOps pipelines)Model versioning and registryAutomated retraining workflowsInfrastructure as code for ML
ML Monitoring and Observability
3-4 weeksSkills You'll Build
Model performance monitoringData drift detectionModel drift and decayAlerting for ML systemsDebugging production ML issues
Portfolio and Career Transition
2-3 weeksSkills You'll Build
Building MLOps portfolio projectsShowcasing testing-to-MLOps transitionMLOps engineer interviewsNetworking in MLOps communityPositioning QA experience for ML